This way, we encourage the latent state to depend mostly on its own actions, thus making it less dependent on others. Consequently, this disentanglement of the agent's latent space allows us to dispatch them separately. We use the lower bound proposed by [1]} to practically maximize mutual information by training an additional neural network to predict previous action from the latent state of the agent:
\(\mathcal {L}_{\text{info}} = -\ln {p_\phi (a^i_{t-1} \mid h^i_t, z^i_t)}\) 
